title: Assets of imputation to ultra-high density for productive and functional traits.
abstract: The aim of this study was to evaluate different-density genotyping panels for genotype imputation and genomic prediction. Genotypes from customized Golden Gate Bovine3K BeadChip [LD3K; low-density (LD) 3,000-marker (3K); Illumina Inc., San Diego, CA] and BovineLD BeadChip [LD6K; 6,000-marker (6K); Illumina Inc.] panels were imputed to the BovineSNP50v2 BeadChip [50K; 50,000-marker; Illumina Inc.]. In addition, LD3K, LD6K, and 50K genotypes were imputed to a BovineHD BeadChip [HD; high-density 800,000-marker (800K) panel], and with predictive ability evaluated and compared subsequently. Comparisons of prediction accuracy were carried out using Random boosting and genomic BLUP. Four traits under selection in the Spanish Holstein population were used: milk yield, fat percentage (FP), somatic cell count, and days open (DO). Training sets at 50K density for imputation and prediction included 1,632 genotypes. Testing sets for imputation from LD to 50K contained 834 genotypes and testing sets for genomic evaluation included 383 bulls. The reference population genotyped at HD included 192 bulls. Imputation using BEAGLE software (http://faculty.washington.edu/browning/beagle/beagle.html) was effective for reconstruction of dense 50K and HD genotypes, even when a small reference population was used, with 98.3% of SNP correctly imputed. Random boosting outperformed genomic BLUP in terms of prediction reliability, mean squared error, and selection effectiveness of top animals in the case of FP. For other traits, however, no clear differences existed between methods. No differences were found between imputed LD and 50K genotypes, whereas evaluation of genotypes imputed to HD was on average across data set, method, and trait, 4% more accurate than 50K prediction, and showed smaller (2%) mean squared error of predictions. Similar bias in regression coefficients was found across data sets but regressions were 0.32 units closer to unity for DO when genotypes were imputed to HD density. Imputation to HD genotypes might produce higher stability in the genomic proofs of young candidates. Regarding selection effectiveness of top animals, more (2%) top bulls were classified correctly with imputed LD6K genotypes than with LD3K. When the original 50K genotypes were used, correct classification of top bulls increased by 1%, and when those genotypes were imputed to HD, 3% more top bulls were detected. Selection effectiveness could be slightly enhanced for certain traits such as FP, somatic cell count, or DO when genotypes are imputed to HD. Genetic evaluation units may consider a trait-dependent strategy in terms of method and genotype density for use in the genome-enhanced evaluations.
authors.affiliation: Departamento de Producción Animal ETSI Agrónomos-Universidad Politécnica de Madrid, Madrid, Spain. joseantonio.jimenez.montero@upm.es
authors.forename: J A
authors.initials: JA
authors.lastname: Jiménez-Montero
authors.forename: D
authors.initials: D
authors.lastname: Gianola
authors.forename: K
authors.initials: K
authors.lastname: Weigel
authors.forename: R
authors.initials: R
authors.lastname: Alenda
authors.forename: O
authors.initials: O
authors.lastname: González-Recio
mesh_terms: D000818:Animals; D002417:Cattle / Q000235:genetics*; D002452:Cell Count / Q000662:veterinary; D005223:Fats / Q000032:analysis; D005260:Female; D005819:Genetic Markers / Q000235:genetics; D005838:Genotype; D007774:Lactation / Q000235:genetics; D008297:Male; D008892:Milk / Q000737:chemistry / Q000166:cytology; D020411:Oligonucleotide Array Sequence Analysis / Q000662:veterinary*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D019655:Quantitative Trait, Heritable
chemical_list: D005223:Fats; D005819:Genetic Markers
keywords: genomic selection; imputation; machine learning; marker density